DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
This is in response to a letter for a patent filed 17 January 2025 in which claims 1-20 were presented for examination. Claims 1-20 are currently pending.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/03/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner. A copy of the PTO-1449 is attached hereto.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more.
Step 1
Claims 1-20 are within the four statutory categories. Claims 1-9 are drawn to a system (i.e. machine); Claims 10-16 are drawn to a method (i.e. process); and Claims 17-20 are drawn to a non-transitory computer readable medium (i.e. manufacture). Therefore, Claims 1-20 are within the four statutory categories.
Step 2A Prong 1
Independent Claims 1, 10, and 17 substantially recite:
receive a request for potential reviewers,
select, based on the request, a plurality of customers who have purchased items but have not provided any review for the purchased items,
compute review probabilities based on feature data related to the plurality of customers and transactions involving the purchased items, wherein each review probability is a probability of a corresponding customer providing a review,
generate, from the plurality of customers, a ranked list of customers based on the review probabilities and reviewer segmentation data, and
transmit the ranked list of customers to be reminded for providing reviews.
The limitations as a whole recite a method of organizing human activity as the processes, under their broadest reasonable interpretation, covers performance of the limitation by “Managing Personal Behavior or Relationships or Interactions Between People” (which includes social activities, teaching, and following rules or instructions); and or “Commercial Interaction”( which includes agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. The processes may also be interpreted as a “Mental Process” (which includes concepts performed in the human mind, such as, observations, evaluations, judgments, and opinions). Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements: “a system,” “a processor,” “a non-transitory memory,” “instructions,” “at least one machine learning model,” and “a computing device”; Claim 10 recites the additional elements: “at least one machine learning model” and “a computing device”; and claim 15 recites the additional elements: “a non-transitory computer-readable medium,” “instructions,” “at least one processor,” “at least one device,” “at least one machine learning model,” and “a computing device” to perform the “receive,” “select,” “compute,” “generate,” and “transmit” steps.
Further, in regards to the “processor ”in claims 1 and 17, the “receive” and “transmit” limitations are just more mere data gathering, and also are characterized as transmitting or receiving data over a network; and insignificant post-solution activity.
The claimed computer components in the steps are recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea (i.e., “processor” performing a generic computer function of “receive,” “select,” “compute,” “generate,” and “transmit”) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Each of the additional limitations is no more than mere instructions to apply the exception using the generic computer components (the processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (the processor, requester device, provider device, user interface). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to
Integration of the abstract idea into a practical application, the additional element of using (“a system,” “a processor,” “a non-transitory memory,” “instructions,” “at least one machine learning model,” and “a computing device” in claim 1; “at least one machine learning model” and “a computing device” in claim 10; and “a non-transitory computer-readable medium,” “instructions,” “at least one processor,” “at least one device,” “at least one machine learning model,” and “a computing device” in claim 17 to perform the “receive,” “select,” “compute,” “generate,” and “transmit” steps amount to no more than mere instructions to apply the exception using a generic computer component. Thus, even when viewed as a whole, nothing in the claims add significantly more (i.e. inventive concept) to the abstract idea. The claims are ineligible.
As per dependent Claims 2, 11, and 18, the recitation of “determining, based on the request, at least one product type”; and “selecting, from a customer pool, the plurality of customers who have purchased items in the at least one product type but have not provided any review for the purchased items, wherein the plurality of customers are selected based on transaction data and user session data associated with the customer pool” are further directed to a method of organizing human activity and/or a mental process as described in claims 1, 10, and 17, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent Claims 3, 12, and 19, the recitations of “generating the feature data based on: customer features, customer action features, benefit affinity data, transaction segmentation data, and reviewer segmentation data”; and “inputting the feature data to compute, for each of the plurality of customers, a review probability that the customer will provide a review within the at least one product type” further directed to a method of organizing human activity and/or a mental process as described in claims 1, 10, and 17, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent Claims 4, 13, and 20, the recitations of “obtaining historical review data of customers within a past time period”; “determining segmentation metrics based on the historical review data, wherein the segmentation metrics comprise: a review frequency indicating how many reviews a customer provided per order during the past time period, and a review recency indicating a time period passed since a last review provided by a customer”; “normalizing each of the segmentation metrics to generate normalized metrics”; “generating buckets based on the normalized metrics”; and “generating the reviewer segmentation data based on the buckets, wherein the reviewer segmentation data includes a plurality of reviewer segments, each of which corresponds to one or more of the buckets” are further directed to a method of organizing human activity and/or a mental process as described in claims 1, 10, and 17 , respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent Claims 5 and 14, the limitation merely narrow the previously recited abstract idea limitations. Dependent Claims 5 and 14, recite “each bucket is associated with a range boundary”; and “the range boundaries of the buckets and a total quantity of the buckets are determined based on seasonal data and event data during the past time period.” For the reasons described above with respect to claims 1 and 10, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent Claims 6 and 15, the recitation of “receive the feature data”; “concatenate features of the feature data to generate concatenated features”; “fit the concatenated features and convert them into a multi-dimensional vector for each of the plurality of customers”; and “predict a review probability for a respective product type” are further directed to a method of organizing human activity and/or a mental process as described in claims 1 and 10, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Further, the recitation, “a multi-task neural network,” “an input later,” “a feature concatenation layer,” “a feed forward layer,” “hidden layers” and “fully connected neural network” are other computer components recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claims 1 and 10, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent Claims 7 and 16, the recitation of “selecting, from the plurality of customers, a list of customers whose review probabilities are higher than a predetermined threshold based on: (1) a product type determined based on the request and (2) a reviewer segment determined based on the request, wherein: the review probabilities are computed with respect to the product type, and the list of customers are selected from the reviewer segment”; and “ranking the list of customers according to their respective review probabilities to generate the ranked list of customers based on budget data” are further directed to a method of organizing human activity and/or a mental process as described in claims 1 and 10, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent Claim 8, the recitations of “determining, based on the request, an instruction for selecting customers, wherein the instruction indicates an exploration of new customers or an exploitation to increase a total number of reviews”; and “determining the reviewer segment based on the instruction” are further directed to a method of organizing human activity and/or a mental process as described in claims 1 and 10, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent Claim 9, the recitations of “send review reminders to customers in the ranked list…” are further directed to a method of organizing human activity and/or a mental process as described in claims 1 and 10, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Further, the recitation, “an email, a text message, an app or a website” are other computer components recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claim 1, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
Dependent Claims 2-9, 11-16, and 18-20 have been given the full two part analysis including analyzing the additional limitations both individually and in combination. Dependent Claims 2-9, 11-16, and 18-20, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea of the independent claims. The dependent claims recite no additional elements that would integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Simply implementing the abstract idea on generic computer components is not a practical application of the judicial exception and does not amount to significantly more than the judicial exception. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Franson et al. (US Patent No. 10,853,355 B1) in view of Kant et al. (US PG Pub. 20230177588 A1) and Runa Ganguli et al., “A Mathematical Recommendation Model to Rank Reviewers Based on Weighted Score for Online Review System”, 05 May 2021; Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 164, pp 317-325, hereinafter referred to as “Ganguli et al.”).
As per claims 1, 10, and 17, Franson et al. discloses a system, computer-implemented method, and non-transitory computer readable medium comprising:
a processor (Franson et al.: col. 2, lines 22-30, a processor); and
a non-transitory memory storing instructions, that when executed (Franson et al.: col. 2, lines 22-30 memory, instructions) , cause the processor to:
receive a request for potential reviewers (Franson et al.: Abstract; FIGS. 20 and 25),
select, based on the request, a plurality of customers who have purchased items but have not provided any review for the purchased items (Franson et al.: col. 20, lines 1-15, At 2504, a determination is made that at least one individual on the received list should be targeted with a review request. A variety of techniques can be used to make this determination. As one example, all potential reviewers received at 2502 could be targeted (e.g., because the list received at 2502 includes an instruction that all members be targeted). As another example, suppose as a result of process 2100, a determination was made that a business would benefit from more reviews on Google Places. At 2504, any members of the list received at 2502 that have Google email addresses (i.e., @gmail.com addresses) are selected at 2504. One reason for such a selection is that the individuals with @gmail.com addresses will be more likely to write reviews on Google Places (because they already have accounts with Google), and (col. 22, lines 43-57, If the potential reviewer opened the email, but didn't click on any links, an alternate message can be included in a follow-up request. If the potential reviewer opened the email and clicked on a link (but did not author a review) another appropriate action can be selected by follow-up engine 2004 as applicable, such as by featuring a different review site, or altering the message included in the request. Another example of a follow-up action includes contacting the potential reviewer using a different contact method than the originally employed one. For example, where a request was originally sent to a given potential reviewer can determine that a follow-up request be sent to the potential reviewer via a social network, or via a physical postcard)
wherein each review probability is a probability of a corresponding customer providing a review (Franson et al.: col. 20, lines 64-76; FIGS. 20 and 25), In various embodiments, review request engine 2002 is configured to predict a likelihood that a potential reviewer will author a review and to determine a number of reviews to request to arrive at a target number of reviews),
transmit to a computing device the ranked list of customers to be reminded for providing reviews (Franson et al.: col. 22, lines 62-col. 23, line 3 For example, based on historical information (whether about the potential reviewer, or based on information pertaining to other reviewers), follow-up engine 2004 may determine that a reminder request (asking that the potential reviewer write a review) should be sent on a particular date and/or at a particular time to increase the likelihood of a review being authored by the potential reviewer).
Franson et al. does not explicitly disclose, however, Kant et al. discloses:
compute, using at least one machine learning model, review probabilities based on feature data related to the plurality of customers and transactions involving the purchased items, (Kant et al.: [0027] In an implementation, the subsystem is a driver model built using the LightGBM model to explain a customer NPS score. More specifically, the fine-tuned subsystem and customer feedback data is used to determine a probability of a promoter rating of the customer(s). The present invention thereafter encompasses using Shap values to understand a contribution of each factor/feature in the determined probability of the promoter rating of the customer(s)); and (Kant et al.: [0037] The sub-system is fine-tuned to determine a probability of a promoter rating for the one or more customers. More specifically, the processing unit [104] is configured to determine the probability of the promoter rating for the one or more customers based on the fine-tuned sub-system and the customer feedback data associated with the one or more customers. In an event, the probability of the promoter rating for one or more customers indicates a probability of the one or more customers to give 4 and/or 5 star rating from a total of 5 stars during one or more surveys). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Franson et al. to include calculating review probabilities in order to determining the contribution of each target (important) feature in the probability of the promoter rating to further identify why a customer score associated with a customer feedback data is high or low (Kant et al.: [0028]).
Franson et al. in view of Kant et al. does not explicitly disclose, however, Ganguli et al. discloses:
generate, from the plurality of customers, a ranked list of customers based on the review probabilities and reviewer segmentation data (Ganguli et al.: page 320, also see Fig. 3: Section 3.2 data Analysis, The ranking of product or reviewer based on review count has got its significance. A reviewer which shares similar review rating with a greater number of other reviewers should be given higher priority compared to one whose rating is in agreement to a smaller number of reviewers. For this particular purpose, the calculated rank acts as the weight. The objective of the work is to rank the reviewer based on two factors. (i) The difference between the individual reviewer rating with the average rating of the product (taken as modulus). Low difference gets higher priority. (ii) If one product has higher number of reviews than another product, the review difference calculated in previous step gets more weightage. We first propose a mechanism to assign weightage to products based on their review count. This Product Rank Based on Review Count (Prod_Rank) is calculated by assigning rank beginning from 1 in ascending order based on product’s review count in descending order. Next, the Weighted Rating Difference (WD) for each product is calculated which gives the difference in rating opinion of a reviewer with respect to other reviewers, weighted with product rank. Weighted Rating Difference (WD) against each Product ID is defined by (1)) {The Examiner interprets this to mean the probability is based on the high review count from the large dataset}. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Franson et al. in view of Kant et al.’s calculating review probabilities to include the ranking of reviewers as taught by Ganguli et al. in order to employ a weighted score scheme based on many parameters such as deviation from average score, assigning more weight to the reviews of a product for which higher numbers of reviews are available by considering other significant attributes like review content, helpfulness votes, and reviewer characteristics (Ganguli et al.: page 324, Section 5, Conclusion and Future Scope).
Claims 2, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Franson et al. (US Patent No. 10,853,355 B1) in view of Kant et al. (US PG Pub. 20230177588 A1) and Runa Ganguli et al., “A Mathematical Recommendation Model to Rank Reviewers Based on Weighted Score for Online Review System”, 05 May 2021; Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 164, pp 317-325, hereinafter referred to as “Ganguli et al.”) as applied to claims 1, 10, and 17 above and in further view of Robinson et al. (US Patent No. 8,108,255 B1).
As per claim 2, 11, and 18, Franson et al. in view of Kant et al. and Ganguli et al. discloses the system, computer-implemented method, and non-transitory computer readable medium of claims 1, 10, and 17, respectively.
Franson et al. in view of Kant et al. and Ganguli et al. does not explicitly disclose, however, Robinson et al. discloses:
wherein the plurality of customers are selected based on:
determining, based on the request, at least one product type (Robinson et al.: col. 5, lines 22-46; For example, the notifications can be triggered by one or more of the following events: change in the user's ranking; change in the user's title; change in another specified user's ranking; change in another specified user's title; surpassing another specified user's ranking; being surpassed by another specified user's ranking; reaching a specified number of points; dropping below a specified number of points; change in the number and/or percentage of helpful votes for the user's first reviews; a user review score falls to a certain level (e.g., 0 points); a determination that an item purchased by the user does not have any reviews on the item web page; availability of bonus points for submitting a first product review for a specified product or class of products (e.g., underwear).; and
selecting, from a customer pool, the plurality of customers who have purchased items in the at least one product type but have not provided any review for the purchased items, wherein the plurality of customers are selected based on transaction data and user session data associated with the customer pool (Robinson et al.: col. 5, lines 22-46 a determination that an item purchased by the user does not have any reviews on the item web page; availability of bonus points for submitting a first product review for a specified product or class of products (e.g., underwear); also see col. 6, lines 53- col. 7 line 11). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Franson et al. in view of Kant et al.’s and Ganguli et al. review probabilities to include the customer poo/grouping as taught by Robinson et al. to encourage users to provide reviews for certain products or product classes that do not have any reviews or that are generally under-reviewed (Robinson et al.: col. 6, lines 45-52).
Prior Art Discussion
Claims 3-9, 12-16, and 19-20 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
As per dependent claims 3-9, 12-16, and 19-20, the best prior art:
1) Franson et al. (US Patent No. 10,853,355 B1) discloses reviewer recommendation by use of a review request engine configured to predict a likelihood that a potential reviewer will author a review and to determine a number of reviews to request to arrive at a target number of reviews.
2) Kant et al. (US PG Pub. 20230177588 A1) discloses a system and method for providing one or more recommendations which includes a sub-system fine-tuned to determine a probability of a promoter rating for the one or more customers.
3) Robinson et al. (US Patent No. 8,108,255 B1) discloses methods and systems for obtaining reviews for items lacking reviews via a first review online game where the information submission may be a review in the form of a textual review, a point review (which may be in the form of a number of stars), a grade review, an icon review (e.g., happy or sad face), an audio review (e.g., recorded live or submitted as an audio file, such as an MP3, ACC, OGG, WAV, AC3, WMA, or other file format), a video review (e.g., recorded live, or submitted as an MPEG, Flash, WMV, AVI, or other file format, and optionally including an audio component), and/or other form of review.
4) Runa Ganguli et al., “A Mathematical Recommendation Model to Rank Reviewers Based on Weighted Score for Online Review System”, 05 May 2021; Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 164, pp 317-325.
However, as per claim 3, 12, and 19, the prior art fails to fairly disclose or teach:
wherein the review probabilities are computed based on:
generating the feature data based on: customer features, customer action features, benefit affinity data, transaction segmentation data, and reviewer segmentation data; and
inputting the feature data into the at least one machine learning model to compute, for each of the plurality of customers, a review probability that the customer will provide a review within the at least one product type.
As per claims 4, 13, and 20, the prior art fails to fairly disclose or teach:
wherein the reviewer segmentation data is generated based on:
obtaining historical review data of customers within a past time period;
determining segmentation metrics based on the historical review data, wherein the segmentation metrics comprise:
a review frequency indicating how many reviews a customer provided per order during the past time period, and
a review recency indicating a time period passed since a last review provided by a customer;
normalizing each of the segmentation metrics to generate normalized metrics;
generating buckets based on the normalized metrics; and
generating the reviewer segmentation data based on the buckets, wherein the reviewer segmentation data includes a plurality of reviewer segments, each of which corresponds to one or more of the buckets.
As per claims 5 and 14, the prior art fails to fairly disclose or teach:
wherein:
each bucket is associated with a range boundary; and
the range boundaries of the buckets and a total quantity of the buckets are determined based on seasonal data and event data during the past time period.
As per claims 6 and 15, the prior art fails to fairly disclose or teach:
wherein the at least one machine learning model comprises a multi-task neural network including:
an input layer configured to receive the feature data;
a feature concatenation layer configured to concatenate features of the feature data to generate concatenated features;
a feed forward layer configured to fit the concatenated features and convert them into a multi-dimensional vector for each of the plurality of customers; and
a plurality of task specific hidden layers each of which corresponds to a respective task for a respective product type, wherein each respective task is performed by a respective fully connected neural network in a corresponding feed forward layer to predict a review probability for a respective product type. ([0070]
As per claims 7 and 16, the prior art fails to fairly disclose or teach:
wherein the ranked list of customers is generated based on:
selecting, from the plurality of customers, a list of customers whose review probabilities are higher than a predetermined threshold based on: (1) a product type determined based on the request and
(2) a reviewer segment determined based on the request, wherein:
the review probabilities are computed with respect to the product type, and the list of customers are selected from the reviewer segment; and
ranking the list of customers according to their respective review probabilities to generate the ranked list of customers based on budget data.
As per claim 8, the prior art fails to fairly disclose or teach:
wherein the reviewer segment is determined based on:
determining, based on the request, an instruction for selecting customers, wherein the instruction indicates an exploration of new customers or an exploitation to increase a total number of reviews; and
determining the reviewer segment based on the instruction
As per claim 9, the prior art fails to fairly disclose or teach:
wherein the instructions, when executed, further cause the processor to: send review reminders to customers in the ranked list, wherein: the review reminders are sent via at least one of: an email, a text message, an app or a website, and
different review reminders are sent in different priorities according to rankings of the customers in the ranked list based on the budget data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Tu et al. (US PG Pub. 2020/0218770 A1) discloses incenting online content creation using machine learning by generating a ranking score based on the relevance value and a product between the feedback sensitivity score and a likelihood of the user providing a feedback signal in relation to the content item.
2) Matthews et al. (US PG Pub. 2024/031999 A1) discloses generating review likelihood for sets of code in which a review likelihood for the potential reviewer of sets of code to be selected to review the set of new code is generated using the machine learning model based on processing the input data. The review system generates an indication of the review likelihood for display in a user interface.
3) Taylor et al.(US PG Pub. 2021/03124993 A1) discloses a system and method for predicting customer behavior where an engagement rank corresponds to a ranked list of customers most likely to engage with the client and may be used by the client to solicit product reviews from the customer or to gather feedback from a new product offering(s).
4) Simon, James (US PG Pub. 20210004855 A1) discloses system and method for receiving real-time consumer transactional feedback where machine learning can be used to identify the likelihood that a particular consumer or consumer type will participate in providing feedback on a transaction as a result of a particular incentive offer.
5) Berry et al. (US Patent No. 10,417,671 B2) discloses optimizing dynamic review generating for redirecting request links for creating and distributing dynamic “review requests” wherein each link may dynamically re-route consumers to the optimal website to improve the quality of review data across the Internet.
6) Na Eun Bok (KR 20200003459 A) discloses a method and system for registering customer reviews based on network
7) Packer et al. (US PG Pub. 20170024753 A1) discloses a system and method for performing a quality assessment by segmenting and analyzing verbatims.
8) Wong, Rick, “Amazon Reviewer Ranking 101: How It Works”, 15 March 2021, sellermetrics.app, 18 pages.
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/F.A.N/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/ Supervisory Patent Examiner, Art Unit 3628
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